Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Write your Algorithm
  • Step 6: Test Your Algorithm

Step 0: Import Datasets

Make sure that you've downloaded the required human and dog datasets:

Note: if you are using the Udacity workspace, you DO NOT need to re-download these - they can be found in the /data folder as noted in the cell below.

  • Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location /dog_images.

  • Download the human dataset. Unzip the folder and place it in the home directory, at location /lfw.

Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.

In [19]:
import numpy as np
from glob import glob

# load filenames for human and dog images
human_files = np.array(glob("data/lfw/*/*"))
dog_files = np.array(glob("data/dog_images/*/*/*"))

# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
There are 13233 total human images.
There are 8351 total dog images.

Step 1: Detect Humans

In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.

OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [4]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[0])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [5]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

haarcascade_frontalface_alt
  • Face Detector Accuracy on Human images : 99.0%
  • Face Detector Accuracy on dog images : 9.0%
FaceNet pytorch
  • Face Detector Accuracy on Human images using DL : 100.0%
  • Face Detector Accuracy on dog images using DL : 20.0%
In [4]:
from tqdm import tqdm

human_files_short = human_files[:100]
dog_files_short = dog_files[:100]

#-#-# Do NOT modify the code above this line. #-#-#

## TODO: Test the performance of the face_detector algorithm 
human_face_human_img_count = 0
for human_img in tqdm(human_files_short):
    human_face_human_img_count += int(face_detector(human_img))

human_face_dog_img_count = 0
for dog_img in tqdm(dog_files_short):
    human_face_dog_img_count += int(face_detector(dog_img))
## on the images in human_files_short and dog_files_short.
100%|██████████| 100/100 [00:01<00:00, 54.88it/s]
100%|██████████| 100/100 [00:08<00:00, 11.13it/s]
In [5]:
print(f" Face Detector Accuracy on Human images : {human_face_human_img_count * 100/len(human_files_short)}%")
print(f" Face Detector Accuracy on dog images : {human_face_dog_img_count * 100/len(dog_files_short)}%")
 Face Detector Accuracy on Human images : 99.0%
 Face Detector Accuracy on dog images : 9.0%

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [6]:
### (Optional) 
### TODO: Test performance of anotherface detection algorithm.
### Feel free to use as many code cells as needed.
import torch

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

from facenet_pytorch import MTCNN
mtcnn = MTCNN(margin= 20, keep_all= True, post_process= False)

def face_detector_dl(img_path):
    img = cv2.imread(img_path)
    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    boxes, probs = mtcnn.detect(rgb)
    return boxes is not None
In [7]:
human_face_human_img_count_dl = 0
for human_img in tqdm(human_files_short):
    human_face_human_img_count_dl += int(face_detector_dl(human_img))

human_face_dog_img_count_dl = 0
for dog_img in tqdm(dog_files_short):
    human_face_dog_img_count_dl += int(face_detector_dl(dog_img))
100%|██████████| 100/100 [00:04<00:00, 23.60it/s]
100%|██████████| 100/100 [00:13<00:00,  7.45it/s]
In [8]:
print(f" Face Detector Accuracy on Human images using DL : {human_face_human_img_count_dl * 100/len(human_files_short)}%")
print(f" Face Detector Accuracy on dog images using DL : {human_face_dog_img_count_dl * 100/len(dog_files_short)}%")
 Face Detector Accuracy on Human images using DL : 100.0%
 Face Detector Accuracy on dog images using DL : 20.0%

Step 2: Detect Dogs

In this section, we use a pre-trained model to detect dogs in images.

Obtain Pre-trained VGG-16 Model

The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.

In [5]:
import torch
import torchvision.models as models

# define VGG16 model
VGG16 = models.vgg16(pretrained=True)

# check if CUDA is available
use_cuda = torch.cuda.is_available()

# move model to GPU if CUDA is available
if use_cuda:
    VGG16 = VGG16.cuda()

Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.

(IMPLEMENTATION) Making Predictions with a Pre-trained Model

In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.

Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.

In [49]:
from PIL import Image
import torchvision.transforms as transforms

def VGG16_predict(img_path):
    '''
    Use pre-trained VGG-16 model to obtain index corresponding to 
    predicted ImageNet class for image at specified path
    
    Args:
        img_path: path to an image
        
    Returns:
        Index corresponding to VGG-16 model's prediction
    '''
    
    ## TODO: Complete the function.
    ## Load and pre-process an image from the given img_path
    ## Return the *index* of the predicted class for that image
    image = Image.open(img_path).convert('RGB')

    preprocess = transforms.Compose([transforms.Resize(256), 
                                    transforms.CenterCrop(224), 
                                    transforms.ToTensor(), 
                                    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                         std=[0.229, 0.224, 0.225])])

    img_tensor = preprocess(image)
    img_tensor = img_tensor.unsqueeze(0)

    VGG16.eval()
    with torch.no_grad():
        pred_prob = VGG16(img_tensor.to(device))
        pred_class = pred_prob.argmax(dim=1).item()
    
    return pred_class

(IMPLEMENTATION) Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).

Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [7]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    ## TODO: Complete the function.
    pred_class = VGG16_predict(img_path)
    return pred_class in range(151,269) # true/false

(IMPLEMENTATION) Assess the Dog Detector

Question 2: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

VGG16
  • Dog Detector Accuracy on Human images using VGG16 : 1.0%
  • Dog Detector Accuracy on dog images using VGG16 : 97.0%
ResNet50
  • Dog Detector Accuracy on Human images using ResNet50 : 1.0%
  • Dog Detector Accuracy on dog images using ResNet50 : 97.0%
Inception_v3
  • Dog Detector Accuracy on Human images using Inception_v3 : 2.0%
  • Dog Detector Accuracy on dog images using Inception_v3 : 95.0%

We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [13]:
### (Optional) 
### TODO: Report the performance of another pre-trained network.
### Feel free to use as many code cells as needed.
from PIL import Image
import torchvision.transforms as transforms

def model_predict(img_path, model, input_size):
    '''
    Use pre-trained VGG-16 model to obtain index corresponding to 
    predicted ImageNet class for image at specified path
    
    Args:
        img_path: path to an image
        
    Returns:
        Index corresponding to VGG-16 model's prediction
    '''
    
    ## TODO: Complete the function.
    ## Load and pre-process an image from the given img_path
    ## Return the *index* of the predicted class for that image
    image = Image.open(img_path)
    
    preprocess = transforms.Compose([transforms.Resize(input_size), 
                                    transforms.CenterCrop(input_size), 
                                    transforms.ToTensor(), 
                                    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                         std=[0.229, 0.224, 0.225])])
    

    img_tensor = preprocess(image)
    img_tensor = img_tensor.unsqueeze(0)

    model.eval()
    with torch.no_grad():
        pred_prob = model(img_tensor.to(device))
        pred_class = pred_prob.argmax(dim=1).item()
    
    return pred_class

### returns "True" if a dog is detected in the image stored at img_path
def dog_detector_model(img_path, model, input_size):
    
    
    ## TODO: Complete the function.
    pred_class = model_predict(img_path, model, input_size)
    return pred_class in range(151,269) # true/false

def initialize_model(model_name):
    if model_name == "resnet":
        """ ResNet 50 """
        
        model = models.resnet50(pretrained=True)
        input_size = 224
        
    elif model_name == "vgg":
        """ VGG16 """
        
        model = models.vgg16(pretrained=True)
        input_size = 224
        
    elif model_name == "inception":
        """ Inception v3 """
        
        model = models.inception_v3(pretrained=True)
        input_size = 299
    else:
        print(f" model name is not recognized please, try again!!")
    
    if use_cuda:
        model = model.cuda()
        
    return model,input_size
In [14]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.

# model_name = "vgg"
for model_name in ["vgg","resnet","inception"]:
    print(f"Getting {model_name} ....")
    model, input_size = initialize_model(model_name)

    human_face_human_img_count_model = 0
    for human_img in tqdm(human_files_short):
        human_face_human_img_count_model += int(dog_detector_model(human_img, model, input_size))

    human_face_dog_img_count_model = 0
    for dog_img in tqdm(dog_files_short):
        human_face_dog_img_count_model += int(dog_detector_model(dog_img, model, input_size) and True)

    print(f" Dog Detector Accuracy on Human images using {model_name} : {human_face_human_img_count_model * 100/len(human_files_short)}%")
    print(f" Dog Detector Accuracy on dog images using {model_name} : {human_face_dog_img_count_model * 100/len(dog_files_short)}%")
    print(f" =====  ")
Getting vgg ....
100%|██████████| 100/100 [00:00<00:00, 120.71it/s]
100%|██████████| 100/100 [00:01<00:00, 72.03it/s]
 Dog Detector Accuracy on Human images using vgg : 1.0%
 Dog Detector Accuracy on dog images using vgg : 97.0%
 =====  
Getting resnet ....
100%|██████████| 100/100 [00:01<00:00, 76.26it/s]
100%|██████████| 100/100 [00:02<00:00, 47.46it/s]
 Dog Detector Accuracy on Human images using resnet : 1.0%
 Dog Detector Accuracy on dog images using resnet : 97.0%
 =====  
Getting inception ....
100%|██████████| 100/100 [00:02<00:00, 43.81it/s]
100%|██████████| 100/100 [00:03<00:00, 32.49it/s]
 Dog Detector Accuracy on Human images using inception : 2.0%
 Dog Detector Accuracy on dog images using inception : 95.0%
 =====  


Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dog_images/train, dog_images/valid, and dog_images/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!

In [6]:
import os
from torchvision import datasets, transforms
from PIL import Image,ImageFile

ImageFile.LOAD_TRUNCATED_IMAGES = True
import torchvision
import torch


### TODO: Write data loaders for training, validation, and test sets

## Specify appropriate transforms, and batch_sizes

stages = ["train", "valid", "test"]
data_dir = "data/dog_images"
use_cuda = torch.cuda.is_available()
device = "cuda" if use_cuda else "cpu"

normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.RandomRotation(20, resample=Image.BILINEAR),
        transforms.ToTensor(),
        normalize
    ]),
    'test': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize
    ]),
    'valid': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize
    ]),
}

img_datasets = { x : datasets.ImageFolder(os.path.join(data_dir,x), data_transforms[x]) for x in stages}
img_dataloaders = {x : torch.utils.data.DataLoader(img_datasets[x], batch_size= 16, shuffle = True) for x in stages}
# , num_workers = 4

class_names = img_datasets["train"].classes
In [7]:
def vis_images(inp, title=None):
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    fig, ax = plt.subplots(figsize=(28, 43))
    ax.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)

# Get a batch of training data
inputs, classes = next(iter(img_dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

vis_images(out, title=[class_names[x] for x in classes])

Question 3: Describe your chosen procedure for preprocessing the data.

  • How does your code resize the images (by cropping, stretching, etc)? What size did you pick for the input tensor, and why?
  • Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc)? If not, why not?

Answer:

1. In training data I am resizing the image by randomly croping the image with 224 width and 224 height values and I picked 224 because it is also being suggested in many pretrained models.

2. In data augmentation , I used horizontal flip and random rotation to train the model.

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. Use the template in the code cell below.

In [8]:
import torch.nn as nn
import torch.nn.functional as F

# define the CNN architecture
class Net(nn.Module):
    ### TODO: choose an architecture, and complete the class
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding = 1)
        self.conv2 = nn.Conv2d(64, 128, 3, padding = 1)
        self.conv3 = nn.Conv2d(128, 256, 3, padding = 1)
        self.conv4 = nn.Conv2d(256, 512, 3, padding = 1)
        self.conv5 = nn.Conv2d(512, 512, 3, padding = 1)
        
        self.ln1 = nn.Linear(512 * 7 * 7,512)
        self.ln2 = nn.Linear(512, 256)
        self.ln3 = nn.Linear(256, len(class_names) )
        
        self.pool = nn.MaxPool2d(2, 2)
        self.dropout = nn.Dropout(0.2)
        
        
        ## Define layers of a CNN
    
    def forward(self, x):
        ## Define forward behavior
        
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = self.pool(F.relu(self.conv4(x)))
        x = self.pool(F.relu(self.conv5(x)))
        
        x = x.view(-1, 512 * 7 * 7)
        
        x = F.relu(self.ln1(x))
        x = self.dropout(x) 
        x = F.relu(self.ln2(x))
        x = self.dropout(x) 
        x = self.ln3(x)
        
        return x

#-#-# You so NOT have to modify the code below this line. #-#-#

# instantiate the CNN
model_scratch = Net()

# move tensors to GPU if CUDA is available
if use_cuda:
    model_scratch.cuda()

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.

Answer:

  • I have Implemented the CNN architecture with 5 Convolutional Layers and 3 Fullyconnected layers, I have also used dropout to reduce the overfitness during the training time.

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.

In [20]:
import torch.optim as optim

### TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss()

### TODO: select optimizer
optimizer_scratch = optim.SGD(model_scratch.parameters(),lr=0.001, momentum=0.9 )

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.

In [10]:
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
    """returns trained model"""
    # initialize tracker for minimum validation loss
    valid_loss_min = np.Inf 
    
    for epoch in range(1, n_epochs+1):
        # initialize variables to monitor training and validation loss
        train_loss = 0.0
        valid_loss = 0.0
        
        ###################
        # train the model #
        ###################
        model.train()
        for batch_idx, (data, target) in enumerate(loaders['train']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## find the loss and update the model parameters accordingly
            
            optimizer.zero_grad()
            
            output = model.forward(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            
            ## record the average training loss, using something like
            train_loss += loss.item()
            if batch_idx % 40 ==0 :
                print(f" \rTraining {batch_idx+1}/{len(loaders['train'])} batche(s) Done ..." , end="" )
            
        train_loss = (1 / len(loaders["train"])) * train_loss
        print(f"Training Done")
            
        ######################    
        # validate the model #
        ######################
        model.eval()
        for batch_idx, (data, target) in enumerate(loaders['valid']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## update the average validation loss
            torch.no_grad()
            
            out = model.forward(data)
            loss = criterion(out, target)
            valid_loss += loss.item()
            if batch_idx % 10 ==0 :
                print(f" \rValidation {batch_idx+1}/{len(loaders['valid'])} batche(s) Done ...", end="")
            
        valid_loss = (1 / len(loaders["valid"])) * valid_loss
        # print training/validation statistics 
        print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
            epoch, 
            train_loss,
            valid_loss
            ))
        
        train_loss = 0
        ## TODO: save the model if validation loss has decreased
        if valid_loss < valid_loss_min:
            print(f" Valid loss decreaced from {valid_loss_min:.2f} to {valid_loss:.2f} ... ")
            print(f" Saving the model ... ")
            valid_loss_min = valid_loss
            state = {
                    'epoch': epoch,
                    'state_dict': model.state_dict(),
                    'optimizer': optimizer.state_dict(),
            }
            torch.save(state , save_path)
        print(f" {epoch}/{n_epochs} epochs Done ... ")
    # return trained model
    return model
In [ ]:
scratch_model_file = "models/model_scratch.pt"

if os.path.isfile(scratch_model):
    scratch_model = torch.load(scratch_model_file)
    model_scratch.load_state_dict(scratch_model["state_dict"])
    optimizer_scratch.load_state_dict(scratch_model["optimizer"])
    print(f" continuing the training model ......")

# train the model
model_scratch = train(100, img_dataloaders, model_scratch, optimizer_scratch, 
                      criterion_scratch, use_cuda, scratch_model_file)
 continuing the training model ......
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 1 	Training Loss: 3.934077 	Validation Loss: 3.743879
 Valid loss decreaced from inf to 3.74 ... 
 Saving the model ... 
 1/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 2 	Training Loss: 3.908916 	Validation Loss: 3.694675
 Valid loss decreaced from 3.74 to 3.69 ... 
 Saving the model ... 
 2/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 3 	Training Loss: 3.890235 	Validation Loss: 3.692177
 Valid loss decreaced from 3.69 to 3.69 ... 
 Saving the model ... 
 3/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 4 	Training Loss: 3.849223 	Validation Loss: 3.674671
 Valid loss decreaced from 3.69 to 3.67 ... 
 Saving the model ... 
 4/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 5 	Training Loss: 3.809758 	Validation Loss: 3.686370
 5/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 6 	Training Loss: 3.813201 	Validation Loss: 3.613566
 Valid loss decreaced from 3.67 to 3.61 ... 
 Saving the model ... 
 6/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 7 	Training Loss: 3.786351 	Validation Loss: 3.635874
 7/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 8 	Training Loss: 3.766269 	Validation Loss: 3.549423
 Valid loss decreaced from 3.61 to 3.55 ... 
 Saving the model ... 
 8/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 9 	Training Loss: 3.754806 	Validation Loss: 3.604645
 9/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 10 	Training Loss: 3.719822 	Validation Loss: 3.467886
 Valid loss decreaced from 3.55 to 3.47 ... 
 Saving the model ... 
 10/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 11 	Training Loss: 3.691857 	Validation Loss: 3.557300
 11/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 12 	Training Loss: 3.656396 	Validation Loss: 3.466418
 Valid loss decreaced from 3.47 to 3.47 ... 
 Saving the model ... 
 12/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 13 	Training Loss: 3.632099 	Validation Loss: 3.379578
 Valid loss decreaced from 3.47 to 3.38 ... 
 Saving the model ... 
 13/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 14 	Training Loss: 3.602013 	Validation Loss: 3.428647
 14/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 15 	Training Loss: 3.568462 	Validation Loss: 3.366425
 Valid loss decreaced from 3.38 to 3.37 ... 
 Saving the model ... 
 15/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 16 	Training Loss: 3.565600 	Validation Loss: 3.391789
 16/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 17 	Training Loss: 3.545039 	Validation Loss: 3.534958
 17/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 18 	Training Loss: 3.491207 	Validation Loss: 3.350677
 Valid loss decreaced from 3.37 to 3.35 ... 
 Saving the model ... 
 18/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 19 	Training Loss: 3.492808 	Validation Loss: 3.336427
 Valid loss decreaced from 3.35 to 3.34 ... 
 Saving the model ... 
 19/100 epochs Done ... 
Training 321/418 batche(s) Done ...      

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.

In [16]:
def test(loaders, model, criterion, use_cuda):

    # monitor test loss and accuracy
    test_loss = 0.
    correct = 0.
    total = 0.

    model.eval()
    for batch_idx, (data, target) in enumerate(loaders['test']):
        # move to GPU
        if use_cuda:
            data, target = data.cuda(), target.cuda()
        # forward pass: compute predicted outputs by passing inputs to the model
        output = model(data)
        # calculate the loss
        loss = criterion(output, target)
        # update average test loss 
        test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
        # convert output probabilities to predicted class
        pred = output.data.max(1, keepdim=True)[1]
        # compare predictions to true label
        correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
        total += data.size(0)
            
    print('Test Loss: {:.6f}\n'.format(test_loss))

    print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
        100. * correct / total, correct, total))
Test results on scratch model
Test Loss: 3.396731

Test Accuracy: 16% (138/836)
In [45]:
# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load(scratch_model_file)["state_dict"])

# call test function    
test(img_dataloaders, model_scratch, criterion_scratch, use_cuda)
Test Loss: 3.396731


Test Accuracy: 16% (138/836)

Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).

If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.

In [27]:
## TODO: Specify data loaders
## Done :-  Already created

(IMPLEMENTATION) Model Architecture

Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.

In [12]:
import torchvision.models as models
import torch.nn as nn

## TODO: Specify model architecture 

model_transfer = models.resnet50(pretrained=True)

# freeze all model parameters
for param in model_transfer.parameters():
    param.requires_grad = False

# new final layer with 133 classes
num_ftrs = model_transfer.fc.in_features
model_transfer.fc = torch.nn.Linear(num_ftrs, 133)

if use_cuda:
    model_transfer = model_transfer.cuda()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

  • To achieve the final CNN architecture, I have downloaded the pretrained ResNet50 models which have all the final parameters and set require gradient to False , because we are not going to train whole network again, to get the classes from 1 to 133 , I have replace the last fc layer with new linear fc with output features as 133 and finally convert it to the cuda type.

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.

In [15]:
criterion_transfer = nn.CrossEntropyLoss()
optimizer_transfer = optim.SGD(model_transfer.fc.parameters(), lr=0.001, momentum=0.9)

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.

In [38]:
transfer_model_file = "models/model_transfer.pt"

if os.path.isfile(transfer_model_file):
    transfer_model = torch.load(transfer_model_file)
    model_transfer.load_state_dict(transfer_model["state_dict"])
    optimizer_transfer.load_state_dict(transfer_model["optimizer"])

# train the model
model_transfer = train(100, img_dataloaders, model_transfer, optimizer_transfer, criterion_transfer, use_cuda, transfer_model_file)
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 1 	Training Loss: 0.928924 	Validation Loss: 0.387824
 Valid loss decreaced from inf to 0.39 ... 
 Saving the model ... 
 1/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 2 	Training Loss: 0.916208 	Validation Loss: 0.392773
 2/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 3 	Training Loss: 0.906214 	Validation Loss: 0.383503
 Valid loss decreaced from 0.39 to 0.38 ... 
 Saving the model ... 
 3/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 4 	Training Loss: 0.889467 	Validation Loss: 0.377067
 Valid loss decreaced from 0.38 to 0.38 ... 
 Saving the model ... 
 4/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 5 	Training Loss: 0.906097 	Validation Loss: 0.381707
 5/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 6 	Training Loss: 0.901394 	Validation Loss: 0.381443
 6/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 7 	Training Loss: 0.896383 	Validation Loss: 0.371931
 Valid loss decreaced from 0.38 to 0.37 ... 
 Saving the model ... 
 7/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 8 	Training Loss: 0.915292 	Validation Loss: 0.369323
 Valid loss decreaced from 0.37 to 0.37 ... 
 Saving the model ... 
 8/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 9 	Training Loss: 0.858894 	Validation Loss: 0.366165
 Valid loss decreaced from 0.37 to 0.37 ... 
 Saving the model ... 
 9/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 10 	Training Loss: 0.887080 	Validation Loss: 0.374772
 10/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 11 	Training Loss: 0.883642 	Validation Loss: 0.373406
 11/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 12 	Training Loss: 0.900446 	Validation Loss: 0.384277
 12/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 13 	Training Loss: 0.883281 	Validation Loss: 0.381002
 13/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 14 	Training Loss: 0.865430 	Validation Loss: 0.374432
 14/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 15 	Training Loss: 0.874641 	Validation Loss: 0.395205
 15/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 16 	Training Loss: 0.852543 	Validation Loss: 0.372100
 16/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 17 	Training Loss: 0.882426 	Validation Loss: 0.358469
 Valid loss decreaced from 0.37 to 0.36 ... 
 Saving the model ... 
 17/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 18 	Training Loss: 0.854525 	Validation Loss: 0.361317
 18/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 19 	Training Loss: 0.857422 	Validation Loss: 0.378263
 19/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 20 	Training Loss: 0.849267 	Validation Loss: 0.363091
 20/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 21 	Training Loss: 0.849332 	Validation Loss: 0.368676
 21/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 22 	Training Loss: 0.845328 	Validation Loss: 0.366358
 22/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 23 	Training Loss: 0.841665 	Validation Loss: 0.364128
 23/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 24 	Training Loss: 0.865684 	Validation Loss: 0.370609
 24/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 25 	Training Loss: 0.838746 	Validation Loss: 0.355424
 Valid loss decreaced from 0.36 to 0.36 ... 
 Saving the model ... 
 25/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 26 	Training Loss: 0.857516 	Validation Loss: 0.373976
 26/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 27 	Training Loss: 0.843694 	Validation Loss: 0.354932
 Valid loss decreaced from 0.36 to 0.35 ... 
 Saving the model ... 
 27/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 28 	Training Loss: 0.839277 	Validation Loss: 0.347921
 Valid loss decreaced from 0.35 to 0.35 ... 
 Saving the model ... 
 28/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 29 	Training Loss: 0.848684 	Validation Loss: 0.351160
 29/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 30 	Training Loss: 0.826817 	Validation Loss: 0.355221
 30/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 31 	Training Loss: 0.812955 	Validation Loss: 0.362085
 31/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 32 	Training Loss: 0.841716 	Validation Loss: 0.352508
 32/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 33 	Training Loss: 0.834823 	Validation Loss: 0.355087
 33/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 34 	Training Loss: 0.844334 	Validation Loss: 0.369789
 34/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 35 	Training Loss: 0.800415 	Validation Loss: 0.352421
 35/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 36 	Training Loss: 0.811644 	Validation Loss: 0.369847
 36/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 37 	Training Loss: 0.822574 	Validation Loss: 0.358569
 37/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 38 	Training Loss: 0.799774 	Validation Loss: 0.344846
 Valid loss decreaced from 0.35 to 0.34 ... 
 Saving the model ... 
 38/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 39 	Training Loss: 0.792961 	Validation Loss: 0.358725
 39/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 40 	Training Loss: 0.809194 	Validation Loss: 0.358227
 40/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 41 	Training Loss: 0.806223 	Validation Loss: 0.367922
 41/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 42 	Training Loss: 0.831931 	Validation Loss: 0.342401
 Valid loss decreaced from 0.34 to 0.34 ... 
 Saving the model ... 
 42/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 43 	Training Loss: 0.796195 	Validation Loss: 0.344024
 43/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 44 	Training Loss: 0.782932 	Validation Loss: 0.363719
 44/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 45 	Training Loss: 0.795883 	Validation Loss: 0.360884
 45/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 46 	Training Loss: 0.821707 	Validation Loss: 0.357147
 46/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 47 	Training Loss: 0.807796 	Validation Loss: 0.355080
 47/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 48 	Training Loss: 0.787287 	Validation Loss: 0.337517
 Valid loss decreaced from 0.34 to 0.34 ... 
 Saving the model ... 
 48/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 49 	Training Loss: 0.802875 	Validation Loss: 0.357830
 49/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 50 	Training Loss: 0.774800 	Validation Loss: 0.346636
 50/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 51 	Training Loss: 0.790361 	Validation Loss: 0.352492
 51/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 52 	Training Loss: 0.794149 	Validation Loss: 0.359312
 52/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 53 	Training Loss: 0.784507 	Validation Loss: 0.355784
 53/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 54 	Training Loss: 0.801203 	Validation Loss: 0.356965
 54/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 55 	Training Loss: 0.796243 	Validation Loss: 0.346318
 55/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 56 	Training Loss: 0.778581 	Validation Loss: 0.357024
 56/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 57 	Training Loss: 0.770290 	Validation Loss: 0.361577
 57/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 58 	Training Loss: 0.801321 	Validation Loss: 0.351134
 58/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 59 	Training Loss: 0.764413 	Validation Loss: 0.358485
 59/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 60 	Training Loss: 0.802804 	Validation Loss: 0.366703
 60/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 61 	Training Loss: 0.789166 	Validation Loss: 0.356134
 61/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 62 	Training Loss: 0.812694 	Validation Loss: 0.367666
 62/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 63 	Training Loss: 0.760722 	Validation Loss: 0.352496
 63/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 64 	Training Loss: 0.799013 	Validation Loss: 0.360303
 64/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 65 	Training Loss: 0.761131 	Validation Loss: 0.342958
 65/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 66 	Training Loss: 0.770013 	Validation Loss: 0.355115
 66/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 67 	Training Loss: 0.745916 	Validation Loss: 0.358682
 67/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 68 	Training Loss: 0.768794 	Validation Loss: 0.344601
 68/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 69 	Training Loss: 0.743107 	Validation Loss: 0.348589
 69/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 70 	Training Loss: 0.764575 	Validation Loss: 0.337178
 Valid loss decreaced from 0.34 to 0.34 ... 
 Saving the model ... 
 70/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 71 	Training Loss: 0.789620 	Validation Loss: 0.370754
 71/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 72 	Training Loss: 0.777288 	Validation Loss: 0.352027
 72/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 73 	Training Loss: 0.764786 	Validation Loss: 0.350357
 73/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 74 	Training Loss: 0.746278 	Validation Loss: 0.366271
 74/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 75 	Training Loss: 0.780471 	Validation Loss: 0.357901
 75/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 76 	Training Loss: 0.756112 	Validation Loss: 0.348559
 76/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 77 	Training Loss: 0.738036 	Validation Loss: 0.354897
 77/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 78 	Training Loss: 0.749780 	Validation Loss: 0.356241
 78/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 79 	Training Loss: 0.740308 	Validation Loss: 0.344502
 79/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 80 	Training Loss: 0.754355 	Validation Loss: 0.349523
 80/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 81 	Training Loss: 0.754313 	Validation Loss: 0.339378
 81/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 82 	Training Loss: 0.764918 	Validation Loss: 0.375880
 82/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 83 	Training Loss: 0.756480 	Validation Loss: 0.336593
 Valid loss decreaced from 0.34 to 0.34 ... 
 Saving the model ... 
 83/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 84 	Training Loss: 0.751276 	Validation Loss: 0.343212
 84/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 85 	Training Loss: 0.756398 	Validation Loss: 0.353329
 85/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 86 	Training Loss: 0.747271 	Validation Loss: 0.344831
 86/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 87 	Training Loss: 0.759611 	Validation Loss: 0.359979
 87/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 88 	Training Loss: 0.732727 	Validation Loss: 0.350561
 88/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 89 	Training Loss: 0.764645 	Validation Loss: 0.338991
 89/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 90 	Training Loss: 0.729439 	Validation Loss: 0.355433
 90/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 91 	Training Loss: 0.737769 	Validation Loss: 0.344790
 91/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 92 	Training Loss: 0.736075 	Validation Loss: 0.338690
 92/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 93 	Training Loss: 0.728342 	Validation Loss: 0.357422
 93/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 94 	Training Loss: 0.732305 	Validation Loss: 0.351091
 94/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 95 	Training Loss: 0.747463 	Validation Loss: 0.347090
 95/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...    Epoch: 96 	Training Loss: 0.742851 	Validation Loss: 0.349293
 96/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 97 	Training Loss: 0.746726 	Validation Loss: 0.357314
 97/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 98 	Training Loss: 0.733131 	Validation Loss: 0.352247
 98/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 99 	Training Loss: 0.738592 	Validation Loss: 0.347470
 99/100 epochs Done ... 
Training 401/418 batche(s) Done ...        Training Done
Validation 51/53 batche(s) Done ...Epoch: 100 	Training Loss: 0.748805 	Validation Loss: 0.350421
 100/100 epochs Done ... 
Out[38]:
<All keys matched successfully>

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.

Test result on transfer model
Test Loss: 0.354656
Test Accuracy: 88% (743/836)
In [21]:
# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load(transfer_model_file)["state_dict"])

test(img_dataloaders, model_transfer, criterion_transfer, use_cuda)
Test Loss: 0.354656


Test Accuracy: 88% (743/836)

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.

In [25]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.


# list of class names by index, i.e. a name can be accessed like class_names[0]
class_names = [item[4:].replace("_", " ") for item in img_datasets['train'].classes]


def predict_breed_transfer(img_path):
    # load the image and return the predicted breed
    
    image = Image.open(img_path).convert('RGB')

    preprocess = transforms.Compose([transforms.Resize(256), 
                                    transforms.CenterCrop(224), 
                                    transforms.ToTensor(), 
                                    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                         std=[0.229, 0.224, 0.225])])

    img_tensor = preprocess(image)
    img_tensor = img_tensor.unsqueeze(0)
    
    model_transfer.eval()
    with torch.no_grad():
        pred_prob = model_transfer(img_tensor.to(device))
        pred_class = pred_prob.argmax(dim=1).item()
    
    return class_names[pred_class]

Step 5: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and human_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [26]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.

def run_app(img_path):
    
    plt.imshow(Image.open(img_path))
    plt.show()
    if face_detector(img_path):
        print(f" Hello human!")
        print(f" You look like a ... ")
        print(f" {predict_breed_transfer(img_path)} ")
    elif dog_detector(img_path):
        print(f" Hello Doggy!") 
        print(f" {predict_breed_transfer(img_path)} ")
    else:
        print(f" Couldn't find any human face or dog!! ")
    print(f"==========================================")
    print("  ")

Step 6: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: (Three possible points for improvement)

The output has around 88% accuracy on given test data, It is atlease good than I expected.
  1. Get more Data of all the different types of breed and also can try different types of data augmentation transformers.
  2. Modifications in Neural Network Architecture like adding batch normalization, more convolutional layers , adjusting dropout percentage etc.
  3. Hyper parameter tuning like tweaking the values of learning rate , momentum, batchsize, epochs etc.
In [52]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.

## suggested code, below
for file in np.hstack((human_files[10:13], dog_files[10:13])):
    actual_label = file.split("/")[-1][:-4]
    print(f"Actual label is {actual_label}")
    run_app(file)
Actual label is Emanuel_Ginobili_0002
 Hello human!
 You look like a ... 
 Dogue de bordeaux 
==========================================
  
Actual label is Emanuel_Ginobili_0004
 Hello human!
 You look like a ... 
 Chinese shar-pei 
==========================================
  
Actual label is Emanuel_Ginobili_0003
 Hello human!
 You look like a ... 
 Dachshund 
==========================================
  
Actual label is German_pinscher_04843
 Hello Doggy!
 Doberman pinscher 
==========================================
  
Actual label is German_pinscher_04838
 Hello Doggy!
 German pinscher 
==========================================
  
Actual label is Beauceron_01319
 Hello Doggy!
 Beauceron 
==========================================
  

Testing Kaggle Data

You can Download the data from https://www.kaggle.com/c/dog-breed-identification/data

In [22]:
import pandas as pd

test_images = glob("kaggle_data/train/*")
labels = pd.read_csv("kaggle_data/labels.csv")
In [43]:
for file_path in test_images[10:16]:
    actual_label = labels.loc[labels.id == file_path.split("/")[-1][:-4],"breed"].item()
    print(f"Actual label is {actual_label}")
    run_app(file_path)
Actual label is standard_poodle
 Hello Doggy!
 Poodle 
==========================================
  
Actual label is german_shepherd
 Hello Doggy!
 German shepherd dog 
==========================================
  
Actual label is siberian_husky
 Hello Doggy!
 Alaskan malamute 
==========================================
  
Actual label is papillon
 Hello Doggy!
 Papillon 
==========================================
  
Actual label is bernese_mountain_dog
 Hello Doggy!
 Bernese mountain dog 
==========================================
  
Actual label is bedlington_terrier
 Hello Doggy!
 Bedlington terrier 
==========================================
  
In [44]:
import numpy as np
from glob import glob

# load filenames
files = np.array(glob("images/*"))
for file_path in files:
    run_app(file_path)
 Hello Doggy!
 Irish red and white setter 
==========================================
  
 Couldn't find any human face or dog!! 
==========================================
  
 Hello Doggy!
 Greyhound 
==========================================
  
 Hello Doggy!
 Labrador retriever 
==========================================
  
 Hello Doggy!
 Brittany 
==========================================
  
 Hello Doggy!
 Labrador retriever 
==========================================
  
 Hello Doggy!
 Labrador retriever 
==========================================
  
 Hello human!
 You look like a ... 
 Chinese crested 
==========================================
  
 Hello Doggy!
 Curly-coated retriever 
==========================================
  
 Hello Doggy!
 Curly-coated retriever 
==========================================
  
In [ ]: